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Deep learning (DL) has seen great success in the computer vision (CV) field,and related techniques have been used in security, healthcare, remote sensing,and many other fields. As a parallel development, visual data has becomeuniversal in daily life, easily generated by ubiquitous low-cost cameras.Therefore, exploring DL-based CV may yield useful information about objects,such as their number, locations, distribution, motion, etc. Intuitively,DL-based CV can also facilitate and improve the designs of wirelesscommunications, especially in dynamic network scenarios. However, so far, suchwork is rare in the literature. The primary purpose of this article, then, isto introduce ideas about applying DL-based CV in wireless communications tobring some novel degrees of freedom to both theoretical research andengineering applications. To illustrate how DL-based CV can be applied inwireless communications, an example of using a DL-based CV with amillimeter-wave (mmWave) system is given to realize optimal mmWavemultiple-input and multiple-output (MIMO) beamforming in mobile scenarios. Inthis example, we propose a framework to predict future beam indices frompreviously observed beam indices and images of street views using ResNet,3-dimensional ResNext, and a long short-term memory network. The experimentalresults show that our frameworks achieve much higher accuracy than the baselinemethod, and that visual data can significantly improve the performance of theMIMO beamforming system. Finally, we discuss the opportunities and challengesof applying DL-based CV in wireless communications.

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges